In:
Geophysical Research Letters, American Geophysical Union (AGU), Vol. 44, No. 24 ( 2017-12-28)
Abstract:
Earth system models (ESMs) and their parameterization schemes can be radically improved by data assimilation and machine learning ESMs can integrate and learn from global observations from space and from local high‐resolution simulations Ensemble Kalman inversion and Markov chain Monte Carlo methods show promise as learning algorithms for ESMs
Type of Medium:
Online Resource
ISSN:
0094-8276
,
1944-8007
DOI:
10.1002/2017GL076101
Language:
English
Publisher:
American Geophysical Union (AGU)
Publication Date:
2017
detail.hit.zdb_id:
2021599-X
detail.hit.zdb_id:
7403-2
SSG:
16,13
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